固定解耦式液压悬置集总参数建模及模型修正

针对经典液压悬置集总参数模型无法准确反映固定解耦式液压悬置动特性的问题,分别提出了一种新的液压悬置集总参数模型和一种经典液压悬置集总参数模型的修正模型。两种模型中均考虑了解耦膜刚度对上液室体积柔度的影响,前者将解耦膜显式等效为一弹簧,增加了解耦膜刚度和等效泵吸面积两个参数;后者在经典集总参数模型基础上,根据解耦膜刚度对上液室体积柔度进行了修正,具有更简洁的表达形式。然后使用有限元分析方法辨识了解耦膜刚度和等效体积柔度,以及其余集总参数。最后将辨识得到的集总参数代入模型中后仿真计算发现,考虑解耦膜刚度以后,两种集总参数模型均可准确地预测固定解耦式液压悬置的动刚度和滞后角,而传统液压悬置的集总参数模型无法准确反映该类型液压悬置动态特性。     

油田年产量预测的数学模型

本文将改进的灰色Verhulst模型用于某油田产量的预测。在平均相对误差达到最小准则或最大相对误差达到最小准则下，分别给出了估计灰色Verhulst模型中参数的线性规划方法。研究表明，模型中的背景值参数和边值修正项对模型的预测精度均有影响，在此基础上，分别以平均相对误差达到最小或最大相对误差达到最小为适应度，提出了基于遗传算法求解最佳背景值参数和最佳边值修正项的方法。最后将改进的灰色Verhulst模型用于某油田产量的实际预测，预测精度很高，结果令人满意。     

基于历史数据集生成情景树的两种新方法?

情景树是描述不确定性决策问题中随机参数的一种有效方式。本文提出了两种基于历史数据集生成情景树的新方法。结合金融背景,给出了如何检测套利机会的线性规划模型。两种新方法在生成情景时能够容易地规避套利机会,并且所生成的情景包含了部分历史数据真实的演化模式,能够比较好地拟合资产收益的经验分布函数;此外,所得情景可以恰当地描述资产收益未来的走势,为动态投资组合选择问题提供了两种有效的情景分析工具。     

一阶超对策模型中结局偏好认知信息集结的模式识别法

针对一类一阶n人超对策模型．提出了一种结局偏好认知信息的集鲒方法．首先通过对单一局中人中不同专家给出的对其他局中人结局偏好信息的不同认知进行关联分析，确定出专家的权重评价值．进而运用模糊模式识别原理，通过建立一个非线性规划模型，给出了一种新的超对策结局偏好认知信息的集结方法．最后。用一个数值例子对文中所提方法进行了说明．     

多自由度振动系统响应集的支撑超平面法

对激励由凸集合建模的线性振动系统提出了响应集的概念，将数学上的微分包含问题简化为求解多自由度振动系统的响应集问题。在此基础上，利用凸集的性质提出了一种求解响应集的支撑超平面法，它是利用响应集的支撑超平面将问题转化为求解一类优化问题的解。对于三种典型的凸集载荷分别给出了各自的响应集，并比较和分析了它们的结果。     

一类多目标模糊系数线性规划问题

讨了一类所系数均为模糊数的多目标线性规划问题，通过对模糊数的比较，将模糊多目标线性规划模型转化为清晰的多目标模型，并应用一种基于线性隶属函数的模糊规划算法求其协调解，最后给出了一个数值例子。     

面向产品族协同优化设计的模糊双层规划模型

针对含有不确定参数的产品族协同优化设计问题中的一种基本类型——产品参数与产品功能之间的映射含有模糊系数并且平台参数和个性化参数间具有一定的主从关系的情形，建立了一种对平台参数和非平台参数进行协同优化的模糊双层线性规划模型，并对其决策机制和求解方法进行了研究，给出一个数值算例。     

一种宽度规格的纸卷下料工艺模型分析

针对纸成卷下料问题，适用线性规划和整数规划的理论，通过对纸成卷下料进行分析建立了一工度规格纸卷下料的整数规划模型，为纸卷的优化下料提供了科学依据。     

利用改进的EKF滤波技术识别成层土集总参数模型的参数

根据非线性系统状态参数估计理论和土-结构系统的实际地震动记录，利用多项式一阶近似来代替EKF滤波技术中的求导线性化过程，来估计土双自由度集总参数模型中各参数值。这是一种比较新的参数在线实时辨识技术，具有运算简单、计算精度较高的优点。以美国Alaska州Anchorage市中心一栋14层办公大楼为算例，结果显示地基土集总参数模型的参数递推结果相当可靠。整个方法易于在MATLAB操作平台上实现。     

液阻悬置惯性通道阻尼特性的实验与计算分析

以一轿车动力总成惯性通道型液阻悬置为研究对象,讨论了线性与非线性集总参数模型和模型中物理参数的特点及应用范围;应用流体力学有限元分析的方法,计算得到了惯性通道两端压力差与其中液体流动流量的关系,设计并制作了实验台,对计算结果进行了验证.利用惯性通道中液体流动的动量方程和惯性通道两端的压力差与其流量的关系,采用最小二乘方法,得到了惯性通道中液体流动的阻尼参数.该方法得到的阻尼参数,在较大激振频率和激振振幅范围内反应了惯性通道的阻尼特性.利用非线性的集总参数模型和本文方法得到的惯性通道阻尼特性参数,对一液阻悬置的动态特性进行了计算分析,计算结果和实验结果具有较好的一致性.     

AN INDEPENDENT VARIABLE CONTROLLED GREY FUZZY LINEAR PROGRAMMING APPROACH FOR WASTE FLOW ALLOCATION PLANNING

This paper proposes an independent variable controlled grey fuzzy linear programming (IVC-GFLP) approach to address uncertainty in optimization processes. The IVC-GFLP method improves upon established grey linear programming (GLP) and ordinary grey fuzzy linear programming (GFLP) methods by introducing independent control variables into model formulations. These variables enable the model to address the independent characteristics of constraint uncertainty well. In this paper, the IVC-GFLP approach is applied to a hypothetical case study of municipal solid waste management. Included comparisons between the IVC-GFLP and GLP/GFLP solutions indicate that the IVC-GFLP approach can provide more realistic and applicable solutions than its counterparts.     

GEOMETRIC PROGRAMMING WITH SEVERAL DISCRETE VARIABLES: ALGORITHMS EMPLOYING GENERALIZED BENDERS' DECOMPOSITION

Geometric programming problems in which several of the variables are restricted to assume either integer values or one of a set of standard sizes are known as Semi-Discrete Geometric Programming problems. In this paper several variations of Generalized Benders' Decomposition are described for these problems and some computational experience is presented.     

物资招标采购中组合预测的线性规划方法

结合企业在物资招标采购中大批量订货时,对物资采购量的供应商合理分配及采购费用问题,利用灰色预测法、自适应滤波法及线性回归预测法进行组合预测确定物资采购量,运用层次分析法确定供应商相应权重的基础上建立线性规划模型,力图达到采购的合理分配和采购费用最低的目的.因此,对于解决企业大批量物资采购招标中供应商的合理分配及供应量的确定,以达到企业采购成本最小化的问题,提出了一种有效的方法.     

基于Kano模型的编程语言在线学习平台功能需求分析

目的 编程语言在线学习平台是学习编程新知识的重要途径。厘清平台各类功能的用户需求属性,对提升平台服务质量至关重要。方法 采用德尔菲法、Kano模型和Better－Worse系数等多种研究方法,明确了编程语言在线学习平台的服务内涵,构建了包括5个维度、26种功能的编程语言在线学习平台功能需求服务体系,对编程语言在线学习平台的用户需求属性进行了科学归类。结论 根据Better-Worse系数值测度的用户满意度指数及其四象限坐标图,将编程语言在线学习平台的26种功能需求,分为期望型功能需求、魅力型功能需求、基本型功能需求、无差异型功能需求。在上述分析结论基础上,针对不同需求的功能属性对用户满意度的影响特征,提出改进编程语言在线学习平台用户需求的差异化服务策略。     

Parametric shape and topology optimization: A new level set approach based on cardinal basis functions

The parametric level set approach is an extension of the conventional level set methods for topology optimization. By parameterizing the level set function, level set methods can be directly coupled with mathematical programming to achieve better numerical robustness and computational efficiency. Moreover, the parametric level set scheme can not only inherit the primary advantages of the conventional level set methods, such as clear boundary representation and the flexibility in handling topological changes, but also alleviate some undesired features from the conventional level set methods, such as the need for reinitialization. However, in the existing radial basis function–based parametric level set method, it is difficult to identify the range of the design variables. Besides, the parametric level set evolution often struggles with large fluctuations during the optimization process. Those issues cause difficulties both in numerical stability and in material property mapping. In this paper, a cardinal basis function is constructed based on the radial basis function partition of unity collocation method to parameterize the level set function. The benefit of using cardinal basis function is that the range of the design variables can now be clearly specified as the value of the level set function. A distance regularization energy functional is also introduced, aiming to maintain the desired signed distance property during the level set evolution. With this desired feature, the level set evolution is stabilized against large fluctuations. In addition, the material properties mapped from the level set function to the finite element model can be more accurate.     

基于灰阶熵的模糊对比度自适应图像增强算法

一般的数字图像存在噪声大、对比度低、边缘模糊等缺陷.为了有效地增强图像的模糊对比度,以满足后续的识别与检测要求,提出了一种基于灰阶熵的模糊对比度自适应图像增强算法.在模糊域内,根据邻域窗口灰阶熵值的大小,合理选取阈值,对阈值两侧的图像像素点进行不同程度的对比度增强处理,实现局部特征的增强.实验结果表明,该方法不仅增强了图像的整体对比度,而且有效地丰富了目标图像的细节信息,并抑制了噪声的放大.     

A consistent grayscale‐free topology optimization method using the level‐set method and zero‐level boundary tracking mesh

This paper proposes a level‐set based topology optimization method incorporating a boundary tracking mesh generating method and nonlinear programming. Because the boundary tracking mesh is always conformed to the structural boundary, good approximation to the boundary is maintained during optimization; therefore, structural design problems are solved completely without grayscale material. Previously, we introduced the boundary tracking mesh generating method into level‐set based topology optimization and updated the design variables by solving the level‐set equation. In order to adapt our previous method to general structural optimization frameworks, the incorporation of the method with nonlinear programming is investigated in this paper. To successfully incorporate nonlinear programming, the optimization problem is regularized using a double‐well potential. Furthermore, the sensitivities with respect to the design variables are strictly derived to maintain consistency in mathematical programming. We expect the investigation to open up a new class of grayscale‐free topology optimization. The usefulness of the proposed method is demonstrated using several numerical examples targeting two‐dimensional compliant mechanism and metallic waveguide design problems. Copyright © 2014 John Wiley & Sons, Ltd.     

A velocity field level set method for shape and topology optimization

In this paper, we propose a new implementation of the level set shape and topology optimization, the velocity field level set method. Therein, the normal velocity field is constructed with specified basis functions and velocity design variables defined on a given set of points that are independent of the finite element mesh. A general mathematical programming algorithm can be employed to find the optimal normal velocities on the basis of the sensitivity analysis. As compared with conventional level set methods, mapping the variational boundary shape optimization problem into a finite‐dimensional design space and the use of a general optimizer makes it more efficient and straightforward to handle multiple constraints and additional design variables. Moreover, the level set function is updated by the Hamilton‐Jacobi equation using the normal velocity field; thus, the inherent merits of the implicit representation is retained. Therefore, this method combines the merits of both the general mathematical programming and conventional level set methods. Integrated topology optimization of structures with embedded components of designable geometries is considered to show the capability of this method to deal with general design variables. Several numerical examples in 2D or 3D design domains illustrate the robustness and efficiency of the method using different basis functions.     

AC network state estimation using linear measurement functions

The real/reactive power and current magnitude measurements can be accounted for in an AC network state estimator using linear measurement functions. The nonlinearity in the conventional AC state estimator equations is transferred from the measurement functions into a system of nonlinear equality constraints which is independent of the measurement set. The new format of equations entails two advantages. First, it can be easily integrated in optimisation routines which employ first- and second-order derivative functions. Second, the linear measurement functions can benefit from scaling techniques which are well documented in the linear programming literature. This research details the implementation of a least absolute value state estimator employing the new format of equations. The optimisation method is based on a primal-dual interior-point method that can accurately account for zero injection measurements and power directions. Numerical testing is used to validate the approach for networks with measurement sets that are (i) conventional and (ii) have a high proportion of current magnitude measurements and power signs.     

Methods for optimum and near optimum disassembly sequencing

This paper considers disassembly sequencing problems subjected to sequence dependent disassembly costs. In practice, the methods for dealing with such problems rely mainly on metaheuristic and heuristic methods, which intrinsically generate suboptimum solutions. Exact methods are NP-hard and therefore unsuitable to most of the practical problems. Nevertheless, it is useful to have exact methods available that can be applied in order to check, at least medium sized problems, to what extent the heuristically obtained solutions deviate from the optimum solution. The existing exact approaches, which are based on integer linear programming (ILP), become unmanageable, even for the cases of modest product complexity. To alleviate this problem to some extent, the iterative method that has been proposed by Lambert (2006) is applied here. This method is based on repeatedly solving a binary integer linear programming (BILP) problem instead of an ILP problem. The method appears to converge sufficiently quickly to be valuable for dealing with medium sized problems. We then use the iterative method for the validation of a new heuristic method that is also proposed in this paper. Finally, both the heuristic and the iterative BILP methods are implemented on a cellphone from practice consisting of 25 components that are represented, according to a set of precedence relationships, via a disassembly precedence graph.